Telecommunication Device Diagnostics Using Configuration Snapshots
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Solution Overview
Problem
RF drive tests for assessing mobile network coverage and quality of service are resource-intensive and time-consuming, and users face challenges in configuring their devices for optimal network performance without operator access privileges.
Innovation Solution
A system and method that continuously collects and validates wireless network signals from subscriber devices, allowing access to both privileged and non-privileged device configuration parameters, performs diagnostics using machine learning, and enables device reconfiguration to identify and resolve suboptimal settings, thereby reducing the need for costly RF drive tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RF drive tests are used to assess mobile network coverage and quality of service, then measurement precision is improved, but loss of time and productivity deteriorate due to resource-intensive and time-consuming nature
Solution Approach 1:
The patent creates virtual copies of drive test functionality by using machine learning models trained on historical drive test data. These models can predict network coverage and quality of service metrics without requiring physical drive tests, thereby replicating the measurement capability while eliminating the time and resource costs of actual drive tests.
Solution Approach 2:
The system performs preliminary actions by continuously collecting and analyzing device configuration parameters and network signal measurements in real-time. This ongoing data collection and machine learning model training prepares the system in advance, so that when network assessment is needed, predictions can be made immediately without requiring new drive tests.
2Ease of operation
If device configuration parameters are made accessible without operator access privileges, then ease of operation is improved, but device complexity increases due to privileged and non-privileged parameter management
Solution Approach 1:
The patent introduces an intermediary layer (the machine learning system and diagnostic tool) that mediates between users and complex device configuration parameters. This intermediary automatically analyzes configurations, identifies suboptimal settings, and provides recommendations or automatic adjustments, shielding users from the complexity of privileged parameter management while still enabling access to configuration capabilities.
Solution Approach 2:
The system enables self-service by allowing users to access and modify device configuration parameters through automated diagnostic tools and machine learning-driven recommendations. The system autonomously identifies issues and guides users through configuration changes without requiring operator intervention, making privileged parameters effectively accessible to end users.
3Productivity
If machine learning diagnostics are implemented for device performance analysis, then productivity is improved by reducing drive tests, but device complexity increases due to continuous data collection and analysis requirements
Solution Approach 1:
The patent implements a universal data collection and analysis system that serves multiple functions: it collects device configuration parameters, monitors network signal measurements, trains machine learning models, performs real-time diagnostics, and generates predictions. This multi-functional system consolidates what would otherwise require separate tools and processes, improving productivity while managing complexity through integration.
Data Source
AI summary
In one embodiment, a server receives a number of first device configuration parameters of a terminal that are retrievable without providing carrier operator access privileges. The server receives a number of second device configuration parameters of the terminal that are retrievable only with carrier operator access privileges. The server receives signal information and network usage information of the terminal. The server determines one or more historical snapshots of first and second device configuration parameters, signal information, and network usage information for the terminal. The server performs diagnostics for the terminal based on the first and second device configuration parameters, signal information, network usage information, and the one or more historical snapshots to identify malfunctions at the terminal.